HyperSolver: A Practical Unified Framework for Large-Scale Combinatorial Optimization
Why this work is in the frame
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Bibliographic record
Abstract
We present HyperSolver, a unified hypergraph neural network framework for solving NP-hard combinatorial optimization problems using a single neural network architecture. Traditional approaches require different algorithms for each problem, while HyperSolver uses the same architecture across multiple minimization and maximization problems, including set cover, hitting set, subset sum, hypergraph max cut, and hypergraph multiway cut. We represent each problem instance as a hypergraph, where hyperedges can connect multiple nodes simultaneously to capture multi-element relationships directly. HyperSolver learns through unsupervised training using problem-specific loss functions without requiring pre-computed solutions or labeled training data. We evaluated HyperSolver on synthetic benchmark datasets with controlled structural parameters and compared its performance to commercial solvers, traditional heuristics, and existing hypergraph neural network methods. HyperSolver consistently computes high-quality solutions with significant speedups over exact methods, traditional heuristics, and competing neural approaches. The framework demonstrates effective knowledge transfer across problem types, where models trained on one problem accelerate training on different problems while maintaining solution quality. These results establish HyperSolver as a practical unified alternative to problem-specific solvers for large-scale combinatorial optimization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it